socioeconomic heterogeneity in model applications (schema) · socioeconomic heterogeneity in model...

Socioeconomic Heterogeneity in Model Applications (SCHEMA)

G. Kiesewetter, N.D. Rao, S. K.C., H. ValinM. Cantele, S. Pachauri, P. Sauer, W. Schoepp, M. Speringer

ENE, ESM, MAG, POP Programs

SAC Meeting - 20/21 April 2015

Different socioeconomic groups have different preferences / consumption patterns

different impacts on the environment

Different socioeconomic groups are impacted differently by the environment

different impacts of the environment on well-being

Why does Socioeconomic Heterogeneity (SEH) matter for systems analysis?

Modelling the “human” in human-environment systems at IIASA

Scenario

Storylines

Population GDP

GLOBIOM (+G4M)integrated

agricultural,

bioenergy and

forestry model

MESSAGE (+Macro, MAGICC)

systems engineering model

(all GHGs and all energy

sectors)

socio-economic drivers

Aggregated Actors

GAINSGHG and air

pollution

mitigation

model

Cooking

demand

iteration

MESSAGE

Access

Fuel

prices

socio-

economic

drivers

Health

Energy access

Food

consumption

Income

Why SEH matters for systems analysis

India, 2004-05

Energy access: diverse stove/fuel usage across income

groups

0

100

200

300

400

500

600

700

800

0

3

6

9

12

Q1 Q2 Q3 Q4 Q5 Q1 Q2 Q3 Q4 Q5

RURAL URBAN

Inco

me

in

2001 U

S$ p

er

cap

ita

Fin

al

en

erg

y in

GJ p

er

cap

ita

Biomass Other Solids Kerosene LPG Electricity Per capita income

Health: Urban concentration increment


GAINS – modelled for 2010


Food consumption: diverse dietary patterns

Undernourished people rising in Africa Rising obesity in Africa

Sou

rce:

FA

O, 2

01

3

Sou

rce:

FA

O, 2

01

2


Fertility: differs between urban and rural areas

India

Sour

ce:

ww

w.c

lipar

tpan

da.c

om, V

isu

aliz

atio

n: M

. S

pe

rin

ge

r &

S.

KC

Urban: 1.81 Rural: 2.54Total Fertility Rate: 2.34

Project objectives

Research question:

How will changing patterns in urbanization and income

distribution influence human consumption and their

associated pressures on the environment and well-being?

How do environmental policies affect different socio-

economic groups, overall equity and social justice?

Project focus

India (for now)

WELL-BEING INDICATORS

SECTORAL PROJECTIONS& MODEL DEVELOPMENT

SYSTEM ANALYSISNEW SEH DIMENSIONS

Literature review

POLICIES

Urbanization

Incomedistribution

Secondarydrivers, projections

Impact analysis

Policy scenarios

SCHEMA PROJECT STRUCTURE

1 2

5

3

4

Improved model responses

NOW

Key tasks, milestones

Development of SEH dimensions

Rural/urban pop projections (POP)

Projections of income inequality (ENE)

Model improvements and projections

Projections of fuel use in different SE groups (ENE)

Projections of food demand in SE groups (ESM)

Refined air pollution & GHG emission calculations for different SE

groups (MAG)

Health impact calculations in GAINS (indoor and outdoor exposure to

PM) split up by SE group (MAG)

New indicators of well-being based on SEH parameters

Policy analysis

Concept: Population Dimensions

12

Fertility MortalityMigration

(ext)

Country • • •

Age • • •

Sex • • •


(ext)

Education

transition

Migration

(int)

Migration

(sub)

Country • • • • • •

Age • • • • • •

Sex • • • • • •

Education • • • • • •

Rural/urban • • ? • • •

Subnational • • ? • • •


(ext)

Education

transition

Migration

(int)

Country • • • • •

Age • • • • •

Sex • • • • •

Education • • • • •

Rural/urban • • ? • •


(ext)

Education

transition

Country • • • •

Age • • • •

Sex • • • •

Education • • • •

Total Fertility Rate in India, by education and place of residence

13

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

Illiterate Some

Primiary

Primary Lower

Secondary

Tenth Grade Twelfth

Grade

University

Tota

l Fe

rtil

ity

Ra

te (c

hil

dre

n p

er

wo

ma

n)

Rural

Urban

14

The differential by education and by rural urban is also different

Concept: Mortality

15

60

62

64

66

68

70

72

74

76

78

80

Lif

e e

xp

ec

tan

cy

at

bir

th (

in y

ea

rs)

Life expectancy at birth for females - States of India, 2013

Urban

Rural

16

Co

nce

pt:

Inte

rnal

Mig

rati

on

So

urc

e: C

en

su

s 2

00

1; V

isu

aliz

atio

n: N

. S

an

de

r, M

. S

pe

rin

ge

r &

S. K

C

17

Education Attainment Progression Ratio

to Primary Completion (India, 2011)

Time

18

Education Attainment Progression Ratio

to Post Secondary Education (India, 2011)

19, date

Population Pyramid

Multi-state Projection

model

Five Dimensions

Data gathering and

estimates

35 states and India

2 places of residence

6 levels of education

>17 age-groups

2 sexes

36*2*6*17*2 = 14688

Scenario Building

Narratives

Assumptions

Multi-state population model

Transition between

states/place of residence

by age, sex, and education

Education transition

Fertility Rates

Life Tables

International migration

20

Methodology: Projection Model

Methodology: Scenario Building

21

SCENARIO:

Fertility

Fertility by age and education (2010-2015) constant, except

Fertility level among women with below primary completion converges

to the fertility level of women with primary completion by 2030

Mortality

Mortality by age and sex (2010-2015) constant

Education

Educational attainment by age and sex (2010)

Trend of education transitions

Migration

Internal Migration Rate by age and sex (1996-2001) –constant

International Migration (X)

22

Methodology: Preliminary Results (Const1)

23, date

Population Pyramid (Const1)

24, date


25, date


26, date


27, date


28, date


29, date










groups (MAG)




Policy analysis

1) Explaining Income Distribution Trends with Petra Sauer, Shonali Pachauri, Jesus Cuaresmo

2) Projecting Income inequality

3) Application to energy/transport demand

Gini Coefficient

𝐺𝑖𝑛𝑖 =𝐴

𝐴 + 𝐵

0 100EqualityLognormal

Distribution

Population (Share)

Income

(Share)

Theoretical Drivers (Indicators Used)

EconomyTFP

Governance

Tax/Expenditure (Social)

Labor support

Political Orientation

Human Capital

Avg Yrs Schooling

Education Gini

Primary/Secondary/Tertiary

Multiple sources

Ghana

Turkey

Philippines

Argentina

Trade

Imports

Exports

High Income vs Low Income countries

Only non-resource (competing) imports

Econometric Model

Generalized least squares

FOCUS: TFP, Education

Controls

Labor Share of income

Governance variables

Detrended, country fixed effects

Global Panel: 1975-2005

𝐼𝑛𝑐𝐼𝑛𝑒𝑞𝑖,𝑡 = 𝛽1 𝑇𝐹𝑃𝑖,𝑡−1 + 𝛽2 𝐸𝑑𝑢𝑐𝑖 ,𝑡−1 + 𝛽3 𝑇𝑟𝑎𝑑𝑒𝑖;𝑡−1 + 𝐶𝑡𝑟𝑦𝑖 + 𝑡 + 𝜀𝑖,𝑡

5,289 obs, 161 countries

Basic quality control

Coverage

(Population, Area, Age)

Unit of Analysis

Time Period

Appropriate,

consistent metric

519 Obs, 48 countries

Source

consistency

Quality Control

DATA METHOD

Sensitivities

➢ Regions

➢ Inc. Ineq. measure

➢ Data source

➢ Model specification

Valid Inferences

➢ Error disturbance corrections

Education GiniAdvanced EconomiesOR

Results Summary

34 36.531.5

Gini - Base Case

TFPGlobal

Imports (Low-skilled)Advanced Economies

Scope of Influence Driver (decadal change)+-

Avg Yrs SchoolingGlobal

Global Tertiary Education

Global Primary Education

SSP1

SSP2

SSP3

SSP1

SSP2

SSP3

TFP

Educ – Avg Attainment

2005=1

Sources: IIASA

Yrs

Actual

SSP3

SSP2

SSP1

Gini Coefficient (Income Inequality) Projections

SSP1

SSP2SSP3

SSP1SSP2

SSP3

TFP

Educ – Avg Attainment

2005=1

Sources: IIASA

Yrs

Actual

SSP3

SSP2

SSP1

Gini Coefficient (Income Inequality) Projections

MESSAGE

ACCESS MODEL

Rural Urban

Fuel

Prices

Cooking

DemandInc

om

e G

rou

ps

SS

Ps

Income

Distributions

Population

Distributions

Household Surveys

Fuel Demand Curves

LPG

Kerosene

LPG

Biomass

Cooking – Modeling Preference Heterogeneity

Frequent

Driver

Average

Driver

Modest

Driver

Light-Duty Vehicle

Consumers/Drivers

Early Adopter Early Majority Late Majority

Urban

Suburban

Rural

Urban

Suburban

Rural

Urban

Suburban

Rural

… … … …… … ……

<= structure repeated =>

Att

itu

de

to

wa

rd

tec

hn

olo

gy/r

isk

Se

ttle

me

nt

Typ

e

Dri

vin

g

Inte

ns

ity

Transport – Preference Heterogeneity









groups (MAG)




Policy analysis

Photos: Peter Menzel, "Hungry Planet: What the World Eats."

Highly heterogeneous food consumption

Between regionsMali

USA

India

Highly heterogeneous food consumption

And within countries…

Photos: Peter Menzel, "Hungry Planet: What the World Eats."

The Craven Family, California

The Fernandez Family, TexasThe Revis Family, North Carolina

1. Identifying sources of heterogeneity

What explains differences in consumption patterns?

Biophysical energy requirements depend on:

Weight and height

Age

Sex

Activity level

Mifflin-St. Jeor Formula:

Resting Energy Expenditure (REE)

= 10 x weight [kg] + 6.25 x height [cm]

- 5 x age [years]

+ 5 for males / - 161 for females

Role of activity level

Source: USDA dietary guidelines

Identifying drivers and determinants

Food Consumption

Heterogeneity

Biophysical

Sex Activity LevelAgeBody Weight &

Composition

Socioeconomic

Educational attainment/

Nutritional knowledge

Income

Occupation

Macro Drivers

Globalization Urban Migration

Sociodemographic

Family

Culture

Gender

Religion

Environmental

Infrastructure Institutional Geographic

Individual Preferences

Palatability PsychologyAttitudes &

Behavior

2. The state of heterogeneity

What data can we use to

assess current levels of

heterogeneity?

Cross country

heterogeneity: FAO Food Balance Sheets

(FBS) (global coverage)

Household Consumer and

Expenditure Surveys 700 surveys available in 116

countries DAFNE network for the EU

Individual dietary surveys

3. Projecting food diets using heterogeneity

Current state of heterogeneity will allow more precise

projections through:

Use of SSP drivers already existing for biophysical

determinants : Age

Sex

Inclusion of environmental factors: Income Socioeconomic status

Urbanisation Consumer preferences

Education (already in SSPs)

Further reading…

First deliverable

forthcoming

Various determinants and

environmental factors

Available data sources

Approaches for

heterogeneity projection









groups (MAG)

Health impact calculations in GAINS (indoor and outdoor exposure

to PM) split up by SE group (MAG)


Policy analysis

Accounting for SEH in GAINS

GAINS calculates health impacts of PM2.5

Clear connection between socioeconomic status and

exposure (e.g. 85% of Indian rural households dependent on

biomass use for cooking in 2011)

New aspects to be considered:

Differentiation of household emissions into SE groups

Differentiation of impacts by SE groups

Outdoor and indoor exposure considered independently so

far – will be merged now

Urbanization trends considered explicitly

Implementation of SSP scenarios in GAINS

Linear transfer coeff (u/r)

Health impact calculations - revised

emissions (u/r, income)

Ambient PM2.5 conc (grid, u/r)

Industrial production, traffic, household fuel use (u/r, income),…

control technologies

Atmospheric transfer coefficient 𝜋(𝑝, 𝑟, 𝑠, 𝑖):

pollutant 𝑝: PPM, SO2, NOx, NH3, VOC source region 𝑟

grid cell 𝑖 SE group 𝑠 (u/r)

Linear transfer coeff (u/r)

Disease specific relative risk functions (nonlinear) = Global Burden of Disease 2010 methodology

Health impact calculations - revised

emissions (u/r, income)

Indoor PM2.5 exposure (u/r, income, sex)

Ambient PM2.5 conc (grid, u/r)

Daily average PM2.5 exposure (u/r, income, sex, grid)

Industrial production, traffic, household fuel use (u/r, income),…

Shortening of life expectancy (u/r, income, sex)

from five different diseases (COPD, IHD, stroke, LC, ALRI)

control technologies

Burnett et al., 2014

PM2.5 [µg/m3]

Rel

ativ

e ri

sk f

or

ALR

I

Status so far

Ambient PM2.5:

Spatial split of population density into rural and urban, merging

information from census and satellite derived urban extends (GRUMP)

Status so far

Ambient PM2.5:



Atmospheric transfer calculations underway (EMEP CTM, global,

0.5×0.5):

Perturbation runs for all pollutants, whole India

Perturbation runs for urban & rural low level sources of PM

Status so far

Ambient PM2.5:



Atmospheric transfer calculations underway (EMEP CTM, global,

0.5×0.5):

Perturbation runs for all pollutants, whole India

Perturbation runs for urban & rural low level sources of PM

Household PM2.5:

Update of calculation methodology to be consistent with Global

Burden of Disease 2010: calculate mortality via indoor concentrations

& disease specific nonlinear relative risk functions

Conclusion and outlook

First year outcomes:

Primary SEH projections:

Income

Rural-urban

Methodologies to incorporate these dimensions into IIASA

models

Next steps:

Generate secondary drivers (food, energy demand)

New indicators of well-being

Policy analysis

Thank you!

Questions…

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